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arXiv 2025年

作者： Taş, Ferhat Ziatdinov, Rushan Department of Computer Science Faculty of Science Istanbul University Istanbul34143 Turkey Department of Industrial Engineering College of Engineering Keimyung University Daegu704-701 Korea Republic of

MSC Codes 53Axx, 65D17Ruled surfaces play an important role in various types of design, architecture, manufacturing, art, and sculpture. They can be created in a variety of ways, which is a topic that has been the subject of much discussion in mathematics and engineering journals. In geometric modelling, ideas are successful if they are not too complex for engineers and practitioners to understand and not too difficult to implement, because these specialists put mathematical theories into practice by implementing them in CAD/CAM systems. Some of these popular systems such as AutoCAD, Solidworks, CATIA, Rhinoceros 3D, and others are based on simple polynomial or rational splines and many other beautiful mathematical theories that have not yet been implemented due to their complexity. Based on this philosophy, in the present work, we investigate a simple way to generate ruled surfaces whose generators are the curvature axes of curves. We show that this type of ruled surface is a developable surface and that there is at least one curve whose curvature axis is a line on the given developable surface. In addition, we discuss the classifications of developable surfaces corresponding to space curves with singularities, while these curves and surfaces are most often avoided in practical design. Our research also contributes to the understanding of the singularities of developable surfaces and, in their visualisation, proposes the use of environmental maps with a circular pattern that creates flower-like structures around the singularities. Copyright © 2025, The Authors. All rights reserved.

关键词： Visualization

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A review: Challenges and opportunities for artificial intelligence and robotics in the offshore wind sector

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Energy and AI 2022年第2期8卷 177-212页

作者： Daniel Mitchell Jamie Blanche Sam Harper Theodore Lim Ranjeetkumar Gupta Osama Zaki Wenshuo Tang Valentin Robu Simon Watson David Flynn Smart Systems Group Institute of SensorsSignals and SystemsSchool of Engineering and Physical SciencesHeriot-Watt UniversityEdinburghEH144ASUnited Kingdom Centre for Mathematics and Computer Science Intelligent and Autonomous Systems GroupCWIAmsterdam1098 XG Netherlands Algorithms Group Faculty of Electrical EngineeringMathematics and Computer Science(EEMCS)Delft University of Technology(TU Delft)Delft2628 XE Netherlands The University of Manchester Department of Electrical and Electronic EngineeringOxford RoadManchesterM139PL United Kingdom James Watt School of Engineering University of GlasgowG128QQUnited Kingdom

The UK has set plans to increase offshore wind capacity from 22GW to 154GW by 2030. With such tremendous growth, the sector is now looking to Robotics and Artificial Intelligence (RAI) in order to tackle lifecycle service barriers as to support sustainable and profitable offshore wind energy production. Today, RAI applications are predominately being used to support short term objectives in operation and maintenance. However, moving forward, RAI has the potential to play a critical role throughout the full lifecycle of offshore wind infrastructure, from surveying, planning, design, logistics, operational support, training and decommissioning. This paper presents one of the first systematic reviews of RAI for the offshore renewable energy sector. The state-of-the-art in RAI is analyzed with respect to offshore energy requirements, from both industry and academia, in terms of current and future requirements. Our review also includes a detailed evaluation of investment, regulation and skills development required to support the adoption of RAI. The key trends identified through a detailed analysis of patent and academic publication databases provide insights to barriers such as certification of autonomous platforms for safety compliance and reliability, the need for digital architectures for scalability in autonomous fleets, adaptive mission planning for resilient resident operations and optimization of human machine interaction for trusted partnerships between people and autonomous assistants. Our study concludes with identification of technological priorities and outlines their integration into a new ‘symbiotic digital architecture’ to deliver the future of offshore wind farm lifecycle management.

关键词： Artificial intelligence Autonomous systems Digitalization Offshore renewable energy Offshore wind farms Robotics

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Block Chain Technology Implementations for Secured Financial Transactions using Cloud Environment 1

Block Chain Technology Implementations for Secured Financial...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Lakshmi, T Vara Raja, Naveen S.M Ramadass, Sudhir Narayanan, Sreekumar Sreenivas, Velagapudi Subramanian, Balambigai Institute of Aeronautical Engineering Department of MBA Hyderabad India Department of Computer Science and Engineering Chilukuru village R R Dist Telangana Moinabad India Sterck Systems Chennai India Botho University Faculty of Engineering and Technology Network Infrastructure and Management Department Gaborone Botswana Dhanekula Institute of Engineering and Technology Department of Computer Science and Engineering Vijajayawada Krishna dt A.P Ganguru India Kongu Engineering College Department of ECE Perundurai638060 India

ISBN: (纸本)9781665456876

The advancement of technology has an influence on consumers' decision to switch from executing your financial information in a conventional way to doing so online. Contracts are encouraged by the use of cloud computers or its key feature of accessibility from everywhere. The enormous amount of data handled by the cloud platform used to facilitate money transfers necessitates maintaining a high degree of security for all those important passwords stored there. Although the conventional authentication methods continue to work well in protecting data in cloud, more sophisticated cyber attacks against cloud-based storage of information still exist. Additionally, using typical cryptographic primitives to secure cloud data requires a significant amount of operation time due to the need to execute verification checks for both valid and illegitimate access control. © 2022 IEEE.

关键词： Authentication

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A Review of an off Grid Solar DC System for Rural Houses 7

A Review of an off Grid Solar DC System for Rural Houses

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7th International Conference on Intelligent Computing and control systems, ICICCS 2023

作者： Pratheeba, C. Muthuvinayagam, M. Siva Ramkumar, M. Rohith Bhat, C. Maniraj, P. Kumar, Neelam Sanjeev Nandha Engineering College Department of Electrical and Electronics Engineering Erode India Mahendra Engineering College Department of Electrical and Electronics Engineering Namakkal Mallasamudram India Karpagam Academy of Higher Education Faculty of Engineering Department of Electrical and Electronics Engineering Coimbatore India SIMATS Saveetha School of Engineering Department of Computer Science and Engineering Chennai India M.Kumarasamy College of Engineering Thalavapalayam Department of Electrical and Electronics Engineering Tamilnadu Karur639113 India SIMATS Saveetha School of Engineering Department of Biomedical Engineering Chennai India

ISBN: (纸本)9798350397253

Despite its importance. more than 800 million people still lack access to electricity. Individuals in many rural areas lack access to a consistent supply of energy due to the high cost of grid extension. Off-grid solar systems enable rural residents to connect to the energy 2rid and power their homes' lights. appliances. and other electronic equipment. This document provides a brief overview of a solar power system that can be used to Dower remote homes. Solar panels. a charge controller. batteries. and an inverter make up the system. Sunlight is converted into direct current by solar panels. which is then stored in batteries. The charge controller regulates the charging and discharging cycles of the battery pack in order to keep the cells healthy and long-lasting. The inverter converts the stored DC electricity into the alternating current (AC) power that appliances and other AC loads require. The proposed method is intended to be simple and low-cost. making it an appropriate option for regions with limited resources and low growth. The system can be customized to meet the exact energy needs of the house by adding additional solar panels or batteries. Furthermore. the structure is made up of discrete components. making it easier to assemble and maintain. Off-grid solar power systems are a cost-effective and efficient wav of bringing electricity to areas that are not connected to the power grid. The proposed system describes the design and installation of a solar-powered. direct current (DC) power source for isolated dwellings. The strategy promotes long-term economic growth and raises the living standards of rural families. © 2023 IEEE.

关键词： Solar panels

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Distributed Sequential Receding Horizon control of Multi-Agent systems Under Recurring Signal Temporal Logic

Distributed Sequential Receding Horizon Control of Multi-Age...

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European control Conference (ECC)

作者： Eleftherios E. Vlahakis Lars Lindemann Dimos V. Dimarogonas Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Thomas Lord Department of Computer Science Viterbi School of Engineering University of Southern California Los Angeles CA USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

We consider the synthesis problem of a multi-agent system under signal temporal logic (STL) specifications representing bounded-time tasks that need to be satisfied recurrently over an infinite horizon. Motivated by the limited approaches to handling recurring STL systematically, we tackle the infinite-horizon control problem with a receding horizon scheme equipped with additional STL constraints that introduce minimal complexity and a backward-reachability-based terminal condition that is straightforward to construct and ensures recursive feasibility. Subsequently, we decompose the global receding horizon optimization problem into agent-level programs the objectives of which are to minimize local cost functions subject to local and joint STL constraints. We propose a scheduling policy that allows individual agents to sequentially optimize their control actions while maintaining recursive feasibility. This results in a distributed strategy that can operate online as a model predictive controller. Last, we illustrate the effectiveness of our method via a multi-agent system example assigned a surveillance task.

关键词： Surveillance Decentralized control Europe Predictive models Fasteners Cost function Infinite horizon

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Retraction Note: A blockchain-based electronic medical records management framework in smart healthcare infrastructure(Multimedia Tools and Applications, (2023), 82, (35419–35437), 10.1007/s11042-023-15152-z)

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Multimedia Tools and Applications 2025年第15期84卷 15625-15625页

作者： Zakzouk, Amaal El-Sayed, Ayman Hemdan, Ezz El-Din Alexandria Higher Institute of Engineering and Technology Alexandria Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menoufia Egypt

The Editor-in-Chief has retracted this article because it contains significant textual overlap with two prior publications with no common authors [1, 2]. The Publisher was not able to confirm a current contact email for Ayman El-Sayed. The remaining authors did not respond to correspondence from the Publisher about this retraction. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

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Hunger games search optimization with deep learning model for sustainable supply chain management

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Discover Internet of Things 2023年第1期3卷 10页

作者： Xu, Zheng Jain, Deepak Kumar Neelakandan, S. Abawajy, Jemal School of Computer and Information Engineering Institute for Artificial Intelligence Shanghai Polytechnic University Shanghai201209 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China School of Artificial Intelligence Dalian China Symbiosis Institute of Technology Symbiosis International University Pune India Department of Computer Science and Engineering R.M.K Engineering College Taluk India Deakin University Geelong Australia

The supply chain network is one of the most important areas of focus in the majority of business circumstances. Blockchain technology is a feasible choice for secure information sharing in a supply chain network. Despite the fact that maintaining security at all levels of the blockchain is difficult, cryptographic methods are commonly used in existing works. Effective supply chain management (SCM) offers various benefits to organizations, such as enhanced customer satisfaction, increased operational efficiency, competitive advantage, and cost reduction. Potential SCM for agricultural and food supply chains needs distributors, coordination and collaboration among farmers, retailers, and stakeholders. The use of technology like Block Chain (BC), sensors, and data analytics, can boost traceability and visibility, decrease waste, and ensure safety and quality throughout the supply chain. Therefore, this study develops a Hunger Games Search Optimization with Deep Learning Model for Sustainable agricultural and food Supply Chain Management (HGSODL-ASCM) technique. The fundamental goal of the HGSODL-ASCM technique is to improve decision-making processes for agricultural and food commodity production and storage in order to optimise revenue. In the provided HGSODL-ASCM technique, a bidirectional long short-term memory (Bi-LSTM) model is built to determine the amount of productivity and storage required to maximise profit. In order to boost the performance of the Bi-LSTM classification process, the HGSO algorithm has been utilized in this work. The presented HGSODL-ASCM technique can independently achieve the SCM policies via interaction with complicated and adaptive environments. A brief set of simulations were performed to ensure the improved performance of the HGSODL-ASCM technique. The simulation results demonstrated how superior the HGSODL-ASCM method is to other methods already in use. © The Author(s) 2023.

关键词： Food supply

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An Improved Convolutional Neural Network for Medical Image Segmentation

An Improved Convolutional Neural Network for Medical Image S...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Obaidi Al-Khafaji, Safaa Hakeem Palle, Kowstubha Thingbaijam, Lenin Prasad, P. Venkata Mallala, Balasubbareddy Pund, Sachin College of Dentistry University of Babylon University of Babylon Computer Science Babylon Iraq Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Hyderabad India Mlcu Faculty It India Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350329773

This paper provides a progressed convolutional neural community (CNN) version for medical picture segmentation. It specializes in improving the accuracy and speed of today's segmenting of scientific photos and improving modern semantic segmentation of first-rate present-day clinical photos. Modern accuracy is improved by introducing pre-processing layer, residual connection, attention module, and up sampling layer to the CNN. The stepped-forward overall performance state-of-the-art pace is achieved through dual-project cutting-edge, wherein the segmentation task is divided into two sequential sub-obligations, modern-day semantic segmentation and shape regression. The improved version is examined on publicly available scientific datasets, achieving 49a2d564f1275e1c4e633abc331547db segmentation performance. The results show that the proposed method is robust, green, and generalizable.. © 2023 IEEE.

关键词： Semantic Segmentation

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Analysis of YouTube Video Comments with NLP Methods

Analysis of YouTube Video Comments with NLP Methods

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Pirnau Mironela Priescu Iustin Priescu Catalina Mihaela Pirnau Claudiu Ghiculescu Liviu Daniel Department of Informatics Faculty of Informatics “Titu Maiorescu” University Bucharest Romania Faculty of Automatic Control and Computer Science National University of Science and Technology POLITEHNICA Bucharest Bucharest Romania Manufacturing Engineering Department Faculty of Industrial Engineering and Robotics National University of Science and Technology POLITEHNICA Bucharest Bucharest Romania

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

This study proposes methods that can be used to examine and interpret comments that users have made after watching videos on YouTube on a particular topic. YouTube tutorials are very popular among young people. They have become an important pillar in informal education, thus contributing to the rapid acquisition of skills and knowledge. Under these circumstances, we were interested in analysing the YouTube videos comments, knowing that the platform has a continuous increase in popularity, which is also due to the opportunities of sharing them. Since ChatGPT-themed YouTube videos have seen a significant surge in popularity since 2022, we were interested to analyse some videos that approach the topics of “ChatGPT, AI clone, AI robot and Deep Learning.”

关键词： Video on demand Social networking (online) Education Tutorials Chatbots Web sites Artificial intelligence

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Exploration and Analysis of Seizure Spikes Through Spectral Domain Transformation

Exploration and Analysis of Seizure Spikes Through Spectral ...

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International Conference on Business Intelligence and Information Technology, BIIT 2023

作者： Najmusseher Nizar Banu, P.K. Azar, Ahmad Taher Kamal, Nashwa Ahmad Alzahrani, Abdulkareem Central Campus Karnataka Bangalore India College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha13518 Egypt Faculty of Engineering Cairo University Giza Egypt Department of Computer Engineering and Science Faculty of Computer Science and Information Technology Al-Baha University Al-Baha Saudi Arabia Prince Sultan University Riyadh Saudi Arabia

ISBN: (纸本)9789819739790

Seizure detection is the most crucial area of investigation when it comes to understanding brain disorders. This proposed research study embarked on an automated model for epileptic seizure diagnosis by means of different kinds of Spectral transformation using EEG inputs from seizure sufferers and healthy subjects. This automated model accommodates non-invasive brain electrical activity monitoring. This method aims to facilitate the analysis and identification of epileptic seizure states since, monitoring and diagnosing such brain electrical activity is a complex task due to its numerous divisions and underlying features. The primary objective of this research study is to distinguish between EEG-based seizures and healthy individuals. To achieve this goal, a combination of spectral transformation and EEG analysis techniques is utilized. These techniques include examining the frequency spectrum, magnitude spectrum, correlation, and T-Distributed Stochastic Neighboring Embedding (T-SNE) analysis. This analysis yields valuable insights from EEG data, refining the input data and making it more suitable for prediction and identification. The model’s performance is evaluated using two distinct datasets: real-time EEG data from individuals experiencing epileptic seizures and EEG data from healthy subjects. These datasets are sourced from the Bangalore EEG Epilepsy Dataset (BEED), India and the BONN epilepsy dataset from the UCI repository. In a comparative study of spectral transformation methods, including Complex Fast Fourier Transform (CFFT) and Real-Valued Fast Fourier Transform (RFFT), it is discovered that reducing the data dimension by using feature extraction is not the optimal approach. This simplification leads to the loss of valuable information. Therefore, preserving the full spectrum of EEG characteristics is crucial for gaining valuable insights into brain neuronal functions, ultimately enabling more accurate seizure prediction. © The Author(s), under exclusi

关键词： Fast Fourier transforms

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